Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Modeling Relationships among Pain and Function in Individuals with Knee Osteoarthritis in the A2CPS Cohort.

The Clinical journal of pain·2026
Same author

Intensity-dependent topographical expansion of sensory representations.

bioRxiv : the preprint server for biology·2026
Same author

Individualised mapping of living human brain mitochondria by MRI reveals signatures of bioenergetic defects.

bioRxiv : the preprint server for biology·2026
Same author

A predictive corticospinal model for pain perception.

Cell reports. Medicine·2026
Same author

Mitoception via the Metabokine GDF15 and Human Health.

Biopsychosocial science and medicine·2026
Same author

Brain neuromarkers predict self- and other-related mentalizing across adult, clinical, and developmental samples.

Nature communications·2026

Related Experiment Video

Updated: Feb 18, 2026

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

1.8K

The relation between statistical power and inference in fMRI.

Henk R Cremers1, Tor D Wager2, Tal Yarkoni3

  • 1Department of Clinical Psychology, University of Amsterdam, Amsterdam, Netherlands.

Plos One
|November 21, 2017
PubMed
Summary

Low statistical power in fMRI studies, especially with small sample sizes, can lead to failed experiments. This research highlights the power problem in brain-behavior correlations, suggesting advanced methods for better results.

More Related Videos

fMRI Validation of fNIRS Measurements During a Naturalistic Task
10:36

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

21.6K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.7K

Related Experiment Videos

Last Updated: Feb 18, 2026

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

1.8K
fMRI Validation of fNIRS Measurements During a Naturalistic Task
10:36

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

21.6K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.7K

Area of Science:

  • Neuroimaging
  • Statistical analysis
  • Cognitive neuroscience

Background:

  • Statistically underpowered studies risk experimental failure, particularly in fMRI due to numerous variables and small sample sizes.
  • The challenge of statistical power is critical for between-subjects effects like group comparisons and brain-behavior correlations in fMRI research.

Purpose of the Study:

  • To clarify the statistical power problem in fMRI by simulating weak diffuse and strong localized brain-behavior correlation scenarios.
  • To evaluate the impact of common sample sizes (n=20-30) on statistical power and the reliability of fMRI findings.

Main Methods:

  • Simulated two scenarios: weak diffuse effects and strong localized effects for brain-behavior correlations.
  • Analyzed statistical power, effect representation, and replication variability for common sample sizes (n=20-30).
  • Compared simulation results with empirical data from the Human Connectome Project.

Main Results:

  • Common sample sizes (n=20-30) exhibit extremely low statistical power in the weak diffuse scenario.
  • Small sample sizes poorly represent true effects and show high variability in replications.
  • Empirical Human Connectome Project data aligns more with the weak diffuse scenario, indicating a widespread power issue.

Conclusions:

  • The statistical power problem is significant in fMRI, particularly for weak, diffuse effects common in real-world data.
  • Increasing sample size, relaxing thresholds, or using region-of-interest approaches have limitations.
  • Model-based prediction and meta-analysis offer promising solutions for enhancing statistical power in fMRI studies.